{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 利用Python实现报表自动化"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 3744 entries, 0 to 3743\n",
      "Data columns (total 7 columns):\n",
      "商品ID    3478 non-null float64\n",
      "类别ID    3478 non-null float64\n",
      "门店编号    3478 non-null object\n",
      "单价      3478 non-null float64\n",
      "销量      3478 non-null float64\n",
      "成交时间    3478 non-null datetime64[ns]\n",
      "订单ID    3478 non-null object\n",
      "dtypes: datetime64[ns](1), float64(4), object(2)\n",
      "memory usage: 204.8+ KB\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "from datetime import datetime\n",
    "data = pd.read_csv(r\"../Data/order-14.1.csv\", sep=\",\",engine = \"python\",encoding=\"gbk\",parse_dates=[\"成交时间\"])\n",
    "data.head()\n",
    "data.info()\n",
    "this_month = data[(data[\"成交时间\"]>= datetime(2018,2,1)) & (data[\"成交时间\"]<= datetime(2018,2,28))]\n",
    "last_month = data[(data[\"成交时间\"]>= datetime(2018,1,1)) & (data[\"成交时间\"]<= datetime(2018,1,31))]\n",
    "same_month = data[(data[\"成交时间\"]>= datetime(2017,2,1)) & (data[\"成交时间\"]<= datetime(2017,2,28))]\n",
    "\n",
    "def get_month_data(data):\n",
    "    sale = (data[\"单价\"]*data[\"销量\"]).sum()\n",
    "    traffic = data[\"订单ID\"].drop_duplicates().count()\n",
    "    s_t = sale/traffic\n",
    "    return (sale,traffic,s_t)\n",
    "sale_1,traffic_1,s_t_1 = get_month_data(this_month)\n",
    "sale_2,traffic_2,s_t_2 = get_month_data(last_month)\n",
    "sale_3,traffic_3,s_t_3 = get_month_data(same_month)\n",
    "\n",
    "report = pd.DataFrame([[sale_1,sale_2,sale_3],\n",
    "                       [traffic_1,traffic_2,traffic_3],\n",
    "                       [s_t_1,s_t_2,s_t_3]],\n",
    "                     columns = [\"本月累计\",\"上月同期\",\"去年同期\"],\n",
    "                     index =[\"销售额\",\"客流量\",\"客单价\"])\n",
    "report\n",
    "\n",
    "#添加同比和环比字段\n",
    "report[\"环比\"] = report[\"本月累计\"]/report[\"上月同期\"] -1\n",
    "report[\"同比\"] = report[\"本月累计\"]/report[\"去年同期\"] -1\n",
    "report\n",
    "report.to_csv(r\"../Data/order.csv\",encoding = \"utf-8-sig\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 自动发送电子邮件"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(221, b'Bye')"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from email.mime.multipart import MIMEMultipart\n",
    "import smtplib\n",
    "from email import encoders\n",
    "from email.header import Header\n",
    "from email.mime.text import MIMEText\n",
    "from email.utils import parseaddr,formataddr\n",
    "from email.mime.application import MIMEApplication\n",
    "\n",
    "#发件人邮箱\n",
    "asender = \"\"\n",
    "\n",
    "#收件人邮箱\n",
    "areceiver = \"\"\n",
    "\n",
    "#抄送人邮箱\n",
    "acc = \"\"\n",
    "\n",
    "#邮件主题\n",
    "asubject = \"这是一份测试邮件\"\n",
    "\n",
    "#发件人地址\n",
    "from_addr = \"\"\n",
    "\n",
    "#邮箱密码（授权码）\n",
    "password = \"\"\n",
    "\n",
    "#邮件设置\n",
    "msg = MIMEMultipart()\n",
    "msg[\"Subject\"] = asubject\n",
    "msg[\"to\"] = areceiver\n",
    "msg[\"Cc\"] = acc\n",
    "msg[\"from\"] = asender\n",
    "\n",
    "#邮件正文\n",
    "body = \"你好，这是一封测试邮件\"\n",
    "\n",
    "#添加邮件正文\n",
    "msg.attach(MIMEText(body,'plain','utf-8'))\n",
    "\n",
    "#添加附件\n",
    "#注意，这里的文件路径是分割线\n",
    "filename = \"测试文档.xlsx\"\n",
    "xlsxpart = MIMEApplication(open(r'C:/Users/Administrator/Excel-Python/Data/测试文档.xlsx','rb').read())\n",
    "xlsxpart.add_header('Content-Disposition', 'attachment',filename=\"测试文档.xlsx\") \n",
    "msg.attach(xlsxpart)\n",
    "\n",
    "\n",
    "#设置邮箱服务器地址及端口\n",
    "smtp_server= \"smtp.qq.com\"\n",
    "server = smtplib.SMTP(smtp_server,25)\n",
    "#打印日志\n",
    "server.set_debuglevel(1)\n",
    "\n",
    "#登录邮箱\n",
    "server.login(from_addr,password)\n",
    "server.sendmail(from_addr,areceiver.split(\",\")+acc.split(\",\"),msg.as_string())\n",
    "\n",
    "#断开服务器连接\n",
    "server.quit()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 超市数据分析"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>商品ID</th>\n",
       "      <th>类别ID</th>\n",
       "      <th>门店编号</th>\n",
       "      <th>单价</th>\n",
       "      <th>销量</th>\n",
       "      <th>成交时间</th>\n",
       "      <th>订单ID</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>30006206</td>\n",
       "      <td>915000003</td>\n",
       "      <td>CDNL</td>\n",
       "      <td>25.23</td>\n",
       "      <td>0.328</td>\n",
       "      <td>2017-01-03 09:56:00</td>\n",
       "      <td>20170103CDLG000210052759</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>30163281</td>\n",
       "      <td>914010000</td>\n",
       "      <td>CDNL</td>\n",
       "      <td>2.00</td>\n",
       "      <td>2.000</td>\n",
       "      <td>2017-01-03 09:56:00</td>\n",
       "      <td>20170103CDLG000210052759</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>30200518</td>\n",
       "      <td>922000000</td>\n",
       "      <td>CDNL</td>\n",
       "      <td>19.62</td>\n",
       "      <td>0.230</td>\n",
       "      <td>2017-01-03 09:56:00</td>\n",
       "      <td>20170103CDLG000210052759</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>29989105</td>\n",
       "      <td>922000000</td>\n",
       "      <td>CDNL</td>\n",
       "      <td>2.80</td>\n",
       "      <td>2.044</td>\n",
       "      <td>2017-01-03 09:56:00</td>\n",
       "      <td>20170103CDLG000210052759</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>30179558</td>\n",
       "      <td>915000100</td>\n",
       "      <td>CDNL</td>\n",
       "      <td>47.41</td>\n",
       "      <td>0.226</td>\n",
       "      <td>2017-01-03 09:56:00</td>\n",
       "      <td>20170103CDLG000210052759</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       商品ID       类别ID  门店编号     单价     销量                成交时间  \\\n",
       "0  30006206  915000003  CDNL  25.23  0.328 2017-01-03 09:56:00   \n",
       "1  30163281  914010000  CDNL   2.00  2.000 2017-01-03 09:56:00   \n",
       "2  30200518  922000000  CDNL  19.62  0.230 2017-01-03 09:56:00   \n",
       "3  29989105  922000000  CDNL   2.80  2.044 2017-01-03 09:56:00   \n",
       "4  30179558  915000100  CDNL  47.41  0.226 2017-01-03 09:56:00   \n",
       "\n",
       "                       订单ID  \n",
       "0  20170103CDLG000210052759  \n",
       "1  20170103CDLG000210052759  \n",
       "2  20170103CDLG000210052759  \n",
       "3  20170103CDLG000210052759  \n",
       "4  20170103CDLG000210052759  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "from datetime import datetime\n",
    "data = pd.read_csv(r\"../Data/order-14.3.csv\", sep=\",\",engine = \"python\",encoding=\"gbk\",parse_dates=[\"成交时间\"])\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- **看那些列别的商品比较热销**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>类别ID</th>\n",
       "      <th>销量</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>240</th>\n",
       "      <td>922000003</td>\n",
       "      <td>425.328</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>239</th>\n",
       "      <td>922000002</td>\n",
       "      <td>206.424</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>251</th>\n",
       "      <td>923000006</td>\n",
       "      <td>190.294</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>216</th>\n",
       "      <td>915030104</td>\n",
       "      <td>175.059</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>238</th>\n",
       "      <td>922000001</td>\n",
       "      <td>121.355</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>367</th>\n",
       "      <td>960000000</td>\n",
       "      <td>121.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>234</th>\n",
       "      <td>920090000</td>\n",
       "      <td>111.565</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>249</th>\n",
       "      <td>923000002</td>\n",
       "      <td>91.847</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>237</th>\n",
       "      <td>922000000</td>\n",
       "      <td>86.395</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>247</th>\n",
       "      <td>923000000</td>\n",
       "      <td>85.845</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          类别ID       销量\n",
       "240  922000003  425.328\n",
       "239  922000002  206.424\n",
       "251  923000006  190.294\n",
       "216  915030104  175.059\n",
       "238  922000001  121.355\n",
       "367  960000000  121.000\n",
       "234  920090000  111.565\n",
       "249  923000002   91.847\n",
       "237  922000000   86.395\n",
       "247  923000000   85.845"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.groupby(\"类别ID\")[\"销量\"].sum().reset_index().sort_values(by=\"销量\",ascending = False).head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- **看那些商品比较畅销**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>商品ID</th>\n",
       "      <th>销量</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>29989059</td>\n",
       "      <td>391.549</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>29989072</td>\n",
       "      <td>102.876</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>469</th>\n",
       "      <td>30022232</td>\n",
       "      <td>101.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>523</th>\n",
       "      <td>30031960</td>\n",
       "      <td>99.998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>29989157</td>\n",
       "      <td>72.453</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>476</th>\n",
       "      <td>30023041</td>\n",
       "      <td>64.416</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>505</th>\n",
       "      <td>30026255</td>\n",
       "      <td>62.375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>29989058</td>\n",
       "      <td>56.052</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>510</th>\n",
       "      <td>30027007</td>\n",
       "      <td>48.757</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>903</th>\n",
       "      <td>30171264</td>\n",
       "      <td>45.000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         商品ID       销量\n",
       "8    29989059  391.549\n",
       "18   29989072  102.876\n",
       "469  30022232  101.000\n",
       "523  30031960   99.998\n",
       "57   29989157   72.453\n",
       "476  30023041   64.416\n",
       "505  30026255   62.375\n",
       "7    29989058   56.052\n",
       "510  30027007   48.757\n",
       "903  30171264   45.000"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.pivot_table(data,index=\"商品ID\",values =\"销量\",aggfunc = \"sum\").reset_index().sort_values(by=\"销量\",ascending = False).head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- **不同门店的销售额占比**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "门店编号\n",
       "CDLG    10908.82612\n",
       "CDNL     8059.47867\n",
       "CDXL     9981.76166\n",
       "Name: 销售额, dtype: float64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[\"销售额\"] = data[\"销量\"]*data[\"单价\"]\n",
    "data.groupby(\"门店编号\")[\"销售额\"].sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "门店编号\n",
       "CDLG    0.376815\n",
       "CDNL    0.278392\n",
       "CDXL    0.344792\n",
       "Name: 销售额, dtype: float64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.groupby(\"门店编号\")[\"销售额\"].sum()/data[\"销售额\"].sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a1ba4ff0f0>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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       " <defs>\r\n",
       "  <style type=\"text/css\">\r\n",
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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
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     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#绘制饼图\n",
    "#导入matplotlib库中的pyplot并起名为plt\n",
    "import matplotlib.pyplot as plt\n",
    "#让画布直接在jupyter Notebook中展示出来\n",
    "%matplotlib inline\n",
    "#解决中文乱码问题\n",
    "plt.rcParams[\"font.sans-serif\"]='SimHei'\n",
    "#解决负号无法正常显示问题\n",
    "plt.rcParams[\"axes.unicode_minus\"]= False\n",
    "#设置为矢量图\n",
    "%config InlineBackend.figure_format = 'svg'\n",
    "#建立画布\n",
    "fig = plt.figure()\n",
    "(data.groupby(\"门店编号\")[\"销售额\"].sum()/data[\"销售额\"].sum()).plot.pie()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- **那些时段是超市客流高峰**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
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     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
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   "source": [
    "#提取小时数\n",
    "data[\"小时\"] = data[\"成交时间\"].map(lambda x:int(x.strftime(\"%H\")))\n",
    "#对小时和订单去重\n",
    "tracffic = data[[\"小时\",\"订单ID\"]].drop_duplicates()\n",
    "#求每个小时的客流量\n",
    "tracffic.groupby(\"小时\")[\"订单ID\"].count()\n",
    "#绘制折线图\n",
    "tracffic.groupby(\"小时\")[\"订单ID\"].count().plot()"
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  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 银行数据分析"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 150000 entries, 0 to 149999\n",
      "Data columns (total 6 columns):\n",
      "用户ID    150000 non-null int64\n",
      "好坏客户    150000 non-null int64\n",
      "年龄      150000 non-null int64\n",
      "负债率     150000 non-null float64\n",
      "月收入     120269 non-null float64\n",
      "家属数量    146076 non-null float64\n",
      "dtypes: float64(3), int64(3)\n",
      "memory usage: 6.9 MB\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 150000 entries, 0 to 149999\n",
      "Data columns (total 6 columns):\n",
      "用户ID    150000 non-null int64\n",
      "好坏客户    150000 non-null int64\n",
      "年龄      150000 non-null int64\n",
      "负债率     150000 non-null float64\n",
      "月收入     150000 non-null float64\n",
      "家属数量    146076 non-null float64\n",
      "dtypes: float64(3), int64(3)\n",
      "memory usage: 6.9 MB\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "from datetime import datetime\n",
    "data = pd.read_csv(r\"../Data/loan.csv\", sep=\",\",engine = \"python\",encoding=\"gbk\")\n",
    "data.info()\n",
    "data.head()\n",
    "#处理月收入缺失值的数据\n",
    "data = data.fillna({\"月收入\":data[\"月收入\"].mean()})\n",
    "data.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- **收入和坏账率的关系**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "月收入\n",
       "(0, 5000]          0.087543\n",
       "(5000, 10000]      0.058308\n",
       "(10000, 15000]     0.041964\n",
       "(15000, 20000]     0.041811\n",
       "(20000, 100000]    0.053615\n",
       "Name: 好坏客户, dtype: float64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "#区间切分\n",
    "cut_bins = [0,5000,10000,15000,20000,100000]\n",
    "income_cut = pd.cut(data[\"月收入\"],cut_bins)\n",
    "income_cut\n",
    "all_income_user = data[\"好坏客户\"].groupby(income_cut).count()\n",
    "bad_income_user = data[\"好坏客户\"].groupby(income_cut).sum()\n",
    "bad_rate = bad_income_user/all_income_user\n",
    "bad_rate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
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   "source": [
    "#绘制图形\n",
    "bad_rate.plot.bar()"
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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- **年龄和坏账率关系**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "年龄\n",
       "(-0.001, 37.0]    0.108201\n",
       "(37.0, 45.0]      0.086841\n",
       "(45.0, 52.0]      0.078956\n",
       "(52.0, 59.0]      0.059600\n",
       "(59.0, 67.0]      0.039205\n",
       "(67.0, 109.0]     0.022498\n",
       "Name: 好坏客户, dtype: float64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv(r\"../Data/loan.csv\", sep=\",\",engine = \"python\",encoding=\"gbk\")\n",
    "age_cut = pd.qcut(data[\"年龄\"],6)\n",
    "all_age_user = data[\"好坏客户\"].groupby(age_cut).count()\n",
    "bad_age_user = data[\"好坏客户\"].groupby(age_cut).sum()\n",
    "bad_rate = bad_age_user/all_age_user\n",
    "bad_rate"
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  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
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      "text/plain": [
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     "metadata": {
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     "output_type": "display_data"
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   ],
   "source": [
    "#绘制图形\n",
    "bad_rate.plot.bar()"
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  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- **家庭人口数量和坏账率关系**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "家属数量\n",
       "0.0     0.058629\n",
       "1.0     0.073529\n",
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       "20.0    0.000000\n",
       "Name: 好坏客户, dtype: float64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_age_user = data.groupby(\"家属数量\")[\"好坏客户\"].count()\n",
    "bad_age_user = data.groupby(\"家属数量\")[\"好坏客户\"].sum()\n",
    "bad_rate = bad_age_user/all_age_user\n",
    "bad_rate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
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    "#绘制图形\n",
    "bad_rate.plot()"
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